Treatment and outcome of Pseudomonas aeruginosa bacteraemia: an antibiotic pharmacodynamic analysis
Bibliographic record
Abstract
OBJECTIVES: To conduct a retrospective study of antibiotic pharmacodynamics in the treatment of Pseudomonas aeruginosa bacteraemia, and to identify pharmacodynamic indices associated with clinical cure. METHODS: Cases of P. aeruginosa bacteraemia were identified, and information related to patient demographics, clinical status, antibiotic treatment and clinical outcome were documented. Anti-pseudomonal therapy was assessed, and concentration versus time profiles were constructed using measured levels for aminoglycosides, or population pharmacokinetic models for other antibiotics. P. aeruginosa isolates from all patients were retrieved and MICs for the anti-pseudomonal agents used to treat the episode of bacteraemia were determined. Patient- and treatment-related factors were tested for associations with clinical outcome using univariate and multivariate analyses. RESULTS: Fifty cases of P. aeruginosa bacteraemia were identified and 38 cases were included in the pharmacodynamic analysis. Eighty-seven percent of patients received an aminoglycoside or ciprofloxacin and 79% received piperacillin or ceftazidime. A majority of patients, 71%, were administered a combination of antibiotics. Treatment outcomes were documented as persistent infection in 21%, death within 2-30 days in 21% and clinical cure in 58% of cases. Peak/MIC (P=0.001) and AUC24/MIC (P=0.002) for aminoglycosides and ciprofloxacin were significant factors in univariate tests. Only peak/MIC was associated independently with treatment outcome (P=0.017) in logistic regression analysis. The predicted probability of cure was > or =90% when peak/MIC was at least 8. CONCLUSION: Pharmacodynamic considerations including aggressive dosing with targeted peak/MICs for aminoglycosides and ciprofloxacin are strongly associated with clinical outcome and essential to the appropriate management of P. aeruginosa bacteraemia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".